While the findings may seem obvious, the Cornell work is actually the first full-scale study to show this behavior in empirical form, the researchers contend. Twitter proved valuable in this study because it captures the affect of the individual in real time, Golder said. Typically, clinical studies are done either by bringing subjects to a lab and watching their behaviors -- an unnatural environment for studying day-to-day activities -- or surveying them, an approach limited by fallibility of the subjects' memories.
Also, some subjects "are just not very good at being aware of what their feelings are," Golder said. "It's a big advantage to access people's words in a setting that is natural and spontaneous."
The work, funded by the U.S. National Science Foundation, was done under a research group led by Macy, which combines sociologists and computer scientists to pursue computer-assisted social science research projects. Golder's background is in linguistics and computer science. Prior to joining Cornell, he worked as a research scientist for Hewlett-Packard.
The project "required some engineering know-how, and that will be something that will have to be more and more important for empiric social sciences," he said.
Other parties have also been investigating this new technique of analyzing human activities through the quantitative analysis of their written output, a practice some scholars call culturonomics. In 2010, Google Labs launched a text analysis tool that allows researchers to execute numerical text analysis against Google's massive store of digitized books, which dates back centuries.
This week, Google incorporated the tool, called NGram Viewer, directly into its Google Books service.
Also this week, Twitter has released the source code for its Storm stream processing engine, data analysis software that should help researchers and other users analyze multiple Twitter feeds as they are updated.
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